Technological advances (such as the development of small, lower power packet data radios), consumer demand (for example, widespread demand for smartphones) and paradigm shifts in how people perceive computers (for example, the move from an “internet of computers” towards an “internet of things (IoT)”) have produced a geometric growth in the number and type of devices which may be considered “networked computers.” Whereas the term “networked computers” previously encompassed a narrow range of devices (for example, laptops, desktops and servers) operating using a limited range of communication protocols, the term now embraces a broad range of heterogeneous computing platforms connected across a variety of networking protocols. Networked computers now include, without limitation, home IoT controllers (for example, the Amazon Echo), gateway devices (for example, the Neuron and Dendrion devices manufactured by V2COM, Inc.), smartphones, as well as desktop, laptop and server computers.
However, much of this newly-created networked computing power is tied to function-specific devices with large amounts of downtime in between use cycles (for example, home automation systems may have significant processing and memory resources to support operations such as voice or face recognition, which go unused during the day, when houses are typically empty). In addition to experiencing large periods of downtime between use cycles, much of this newly-created networking computing power is balkanized and connected only to a small number of devices within a tiny network and to a vendor's server. For example, a home automation controller may likely only be communicatively connected to IoT devices within a home, one or two recognized user terminals (e.g., smartphones, tablets or laptop computers) and a manufacturer's server (for example, a server hosted by Amazon or Google).
The above-described proliferation of networked computers presents a wealth of untapped computing power to support distributed computing on a previously unimaginable scale. However, seizing this bounty of latent computational horsepower presents a number of technical challenges, such as how to achieve efficient allocation of heterogeneous distributed processing resources, how to optimally onboard diffuse computing resources as nodes of a distributed network and how to regulate access and maintain the security of private data on devices which are nodes of the network.
Embodiments as described herein are addressed to providing systems, methods and data structures by which diffuse, heterogeneous networked computers can be brought together as a federation of secure nodes of a self-optimizing processing network.